Galveia et al., 2018 - Google Patents
Computer aided diagnosis in ophthalmology: Deep learning applicationsGalveia et al., 2018
- Document ID
- 11264122900580639249
- Author
- Galveia J
- Travassos A
- Quadros F
- da Silva Cruz L
- Publication year
- Publication venue
- Classification in BioApps: Automation of Decision Making
External Links
Snippet
The automated diagnosis of ophthalmologic diseases to assist the medical ophthalmologist in their daily practice is the subject of much research. Recently, image processing based on very deep and complex processing structures became the focus of renewed interest, mostly …
- 238000004195 computer-aided diagnosis 0 title description 3
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00597—Acquiring or recognising eyes, e.g. iris verification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K2209/05—Recognition of patterns in medical or anatomical images
- G06K2209/051—Recognition of patterns in medical or anatomical images of internal organs
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Stolte et al. | A survey on medical image analysis in diabetic retinopathy | |
Gegundez-Arias et al. | A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified U-Net model | |
Almotiri et al. | Retinal vessels segmentation techniques and algorithms: a survey | |
Uysal et al. | Computer-aided retinal vessel segmentation in retinal images: convolutional neural networks | |
Welikala et al. | Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy | |
Singh et al. | Deep learning system applicability for rapid glaucoma prediction from fundus images across various data sets | |
Dayana et al. | An enhanced swarm optimization-based deep neural network for diabetic retinopathy classification in fundus images | |
Khandouzi et al. | Retinal vessel segmentation, a review of classic and deep methods | |
de Moura et al. | Joint diabetic macular edema segmentation and characterization in OCT images | |
Khanna et al. | Deep learning based computer-aided automatic prediction and grading system for diabetic retinopathy | |
Qin et al. | A review of retinal vessel segmentation for fundus image analysis | |
Mansour et al. | Glaucoma detection using novel perceptron based convolutional multi-layer neural network classification | |
Fraz et al. | Computational methods for exudates detection and macular edema estimation in retinal images: a survey | |
Kumar et al. | Analysis of retinal blood vessel segmentation techniques: a systematic survey | |
Suchetha et al. | Region of interest-based predictive algorithm for subretinal hemorrhage detection using faster R-CNN | |
Galveia et al. | Computer aided diagnosis in ophthalmology: Deep learning applications | |
Guergueb et al. | A review of deep learning techniques for glaucoma detection | |
Morales-Lopez et al. | Cataract detection and classification systems using computational intelligence: A survey | |
Samant et al. | A hybrid filtering-based retinal blood vessel segmentation algorithm | |
Pavani et al. | Robust semantic segmentation of retinal fluids from SD-OCT images using FAM-U-Net | |
Langarizadeh et al. | Decision support system for age-related macular degeneration using convolutional neural networks | |
Godishala et al. | Survey on retinal vessel segmentation | |
Tavakoli | Automated optic disk detection in fundus images using a combination of deep learning and local histogram matching | |
DEVI et al. | IMPLEMENTING RESNET-50 TRANSFER LEARNING MODEL FOR DIAGNOSING OCT IMAGES FOR DETECTING AND CLASSIFYING DME ABNORMALITIES | |
Bhardwaj et al. | A computational framework for diabetic retinopathy severity grading categorization using ophthalmic image processing |